# Finding fault types of BLDC motors within UAVs using machine learning techniques

## TL;DR

This paper introduces a machine learning method to detect faults in BLDC motors used in drones, comparing three models for accuracy.

## Contribution

A novel machine learning approach for predicting BLDC motor faults in UAVs is proposed and evaluated.

## Key findings

- Three machine learning models were compared for predicting BLDC motor defects.
- The method aims to improve fault detection accuracy in UAV propulsion systems.
- Results show the potential of machine learning in enhancing UAV motor reliability.

## Abstract

Due to the potential of the Unmanned Aerial Vehicle (UAV), they began to be increasingly used in various fields such as: environment, leisure, health, military, transport, etc. Along with increasing battery storage capacity, the UAVs began to be propulsion by Brushless DC (BLDC) motors. Failure of BLDC motors can lead to loss of control, which can cause accidents. In these conditions, it is necessary to devise methods that can find the defects of the BLDC motors in the UAVs. In this article, the authors propose a novel method to predict BLDC motor defects using machine learning. To maximize the method results, the performance of three machine learning models, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Bayesian Network (BN) in predicting the flaws of BLDC motors, have been compared.